Recent Publications

Frequent usage of complex expressions with numbers and of the terms that require domain knowledge makes it more difficult to comprehend and summarize financial news articles than that of other daily news articles. We present a transformer-based model for the automatic summarization of the financial news articles in Korean and address related issues, and in particular analyze the interplay between the domain of the dataset used for pre-training and that for fine-tuning. We find that the summarization model performs much better when the two coincide, even when they are different from that of the target task, which is the financial domain in our work.

As users in online communities suffer from severe side effects of abusive language, many researchers attempted to detect abusive texts from social media, presenting several datasets for such detection. However, none of them contain both comprehensive labels and contextual information, which are essential for thoroughly detecting all kinds of abusiveness from texts, since datasets with such fine-grained features demand a significant amount of annotations, leading to much increased complexity. In this paper, we propose a Comprehensive Abusiveness Detection Dataset (CADD), collected from the English Reddit posts, with multifaceted labels and contexts. Our dataset is annotated hierarchically for an efficient annotation through crowdsourcing on a large-scale. We also empirically explore the characteristics of our dataset and provide a detailed analysis for novel insights. The results of our experiments with strong pre-trained natural language understanding models on our dataset show that our dataset gives rise to meaningful performance, assuring its practicality for abusive language detection.